Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework
Abstract Background Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory system, with high incidence and mortality rates. Cellular senescence (CS) widely affects the tumor microenvironment (TME) and tumor growth, and is related to the invasion and immune escape of tumor cells...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-02262-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849734765124517888 |
|---|---|
| author | Ting Ge Guixin He Qian Cui Shuangcui Wang Zekun Wang Yingying Xie Yuanyuan Tian Juyue Zhou Jianchun Yu Jinmin Hu Wentao Li |
| author_facet | Ting Ge Guixin He Qian Cui Shuangcui Wang Zekun Wang Yingying Xie Yuanyuan Tian Juyue Zhou Jianchun Yu Jinmin Hu Wentao Li |
| author_sort | Ting Ge |
| collection | DOAJ |
| description | Abstract Background Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory system, with high incidence and mortality rates. Cellular senescence (CS) widely affects the tumor microenvironment (TME) and tumor growth, and is related to the invasion and immune escape of tumor cells. This study aims to develop a robust CS-related signature of LUAD. Methods Using the GSE140797, GSE42458, GSE75037, and GSE85841 datasets, in combination with cellular senescence databases, 75 LUAD CS-related differentially expressed genes (LUAD-CSDEGs) were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. A LUAD-CSRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Finally, the difference of immune infiltration and response to immunotherapy in patients with high and low risk of LUAD were evaluated. Results Based on a 113-combination machine learning framework, we finally identified a LUAD-CSRS containing eight genes: RECQL4, TIMP1, ANLN, SFN, MDK, KIF2C, AGR2, ITGB4. We also confirmed that it was significantly associated with survival, immune cell infiltration, prognosis, and response to immunotherapy in LUAD patients. Additionally, we found it is related to the activation of immune responses and may be involved in regulating the balance between immune cells in the TME. Conclusion In summary, our study constructed a novel LUAD-CSRS, which is not only expected to be a powerful tool for assisting diagnosis and prognosis evaluation of LUAD, but also may provide guidance for personalized immunotherapy programs. |
| format | Article |
| id | doaj-art-bce71f0bc69d42649444989ee3011222 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-bce71f0bc69d42649444989ee30112222025-08-20T03:07:43ZengSpringerDiscover Oncology2730-60112025-04-0116111910.1007/s12672-025-02262-3Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning frameworkTing Ge0Guixin He1Qian Cui2Shuangcui Wang3Zekun Wang4Yingying Xie5Yuanyuan Tian6Juyue Zhou7Jianchun Yu8Jinmin Hu9Wentao Li10Central Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineDepartment of Biostatistics, School of Global Public Health, New York UniversityCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineCentral Laboratory, First Teaching Hospital of Tianjin University of Traditional Chinese MedicineDepartment of Oncology, Macheng People’s HospitalNational Clinical Research Center for Chinese Medicine Acupuncture and MoxibustionAbstract Background Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory system, with high incidence and mortality rates. Cellular senescence (CS) widely affects the tumor microenvironment (TME) and tumor growth, and is related to the invasion and immune escape of tumor cells. This study aims to develop a robust CS-related signature of LUAD. Methods Using the GSE140797, GSE42458, GSE75037, and GSE85841 datasets, in combination with cellular senescence databases, 75 LUAD CS-related differentially expressed genes (LUAD-CSDEGs) were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. A LUAD-CSRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Finally, the difference of immune infiltration and response to immunotherapy in patients with high and low risk of LUAD were evaluated. Results Based on a 113-combination machine learning framework, we finally identified a LUAD-CSRS containing eight genes: RECQL4, TIMP1, ANLN, SFN, MDK, KIF2C, AGR2, ITGB4. We also confirmed that it was significantly associated with survival, immune cell infiltration, prognosis, and response to immunotherapy in LUAD patients. Additionally, we found it is related to the activation of immune responses and may be involved in regulating the balance between immune cells in the TME. Conclusion In summary, our study constructed a novel LUAD-CSRS, which is not only expected to be a powerful tool for assisting diagnosis and prognosis evaluation of LUAD, but also may provide guidance for personalized immunotherapy programs.https://doi.org/10.1007/s12672-025-02262-3Cellular senescenceLung adenocarcinomaMachine learningBioinformatics analysisImmunotherapy |
| spellingShingle | Ting Ge Guixin He Qian Cui Shuangcui Wang Zekun Wang Yingying Xie Yuanyuan Tian Juyue Zhou Jianchun Yu Jinmin Hu Wentao Li Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework Discover Oncology Cellular senescence Lung adenocarcinoma Machine learning Bioinformatics analysis Immunotherapy |
| title | Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework |
| title_full | Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework |
| title_fullStr | Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework |
| title_full_unstemmed | Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework |
| title_short | Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework |
| title_sort | identification of cellular senescence associated genes for predicting the diagnosis prognosis and immunotherapy response in lung adenocarcinoma via a 113 combination machine learning framework |
| topic | Cellular senescence Lung adenocarcinoma Machine learning Bioinformatics analysis Immunotherapy |
| url | https://doi.org/10.1007/s12672-025-02262-3 |
| work_keys_str_mv | AT tingge identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT guixinhe identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT qiancui identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT shuangcuiwang identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT zekunwang identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT yingyingxie identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT yuanyuantian identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT juyuezhou identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT jianchunyu identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT jinminhu identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework AT wentaoli identificationofcellularsenescenceassociatedgenesforpredictingthediagnosisprognosisandimmunotherapyresponseinlungadenocarcinomaviaa113combinationmachinelearningframework |